Generating Paired Transliterated-cognates Using Multiple Pronunciation Characteristics from Web corpora

A novel approach to automatically extracting paired transliterated-cognates from Web corpora is proposed in this paper. One of the most important issues addressed is that of taking multiple pronunciation characteristics into account. Terms from various languages may pronounce very differently. Incorporating the knowledge of word origin may improve the pronunciation accuracy of terms. The accuracy of generated phonetic information has an important impact on term transliteration and hence transliterated-term extraction. Transliterated-term extraction is a fundamental task in natural language processing to extract paired transliterated-terms in studying term transliteration. An experiment on transliterated-term extraction from two kinds of Web resources, Web pages and anchored texts, has been conducted and evaluated. The experimental results show that many transliterated-term pairs, which cannot be extracted using the approach only exploiting English pronunciation characteristics, have been successfully extracted using the proposed approach in this paper. By taking multiple language-specific pronunciation transformations into account may further improve the output of the transliterated-term extraction

A Comparison of Different Machine Transliteration Models

Machine transliteration is a method for automatically converting words in one language into phonetically equivalent ones in another language. Machine transliteration plays an important role in natural language applications such as information retrieval and machine translation, especially for handling proper nouns and technical terms. Four machine transliteration models -- grapheme-based transliteration model, phoneme-based transliteration model, hybrid transliteration model, and correspondence-based transliteration model -- have been proposed by several researchers. To date, however, there has been little research on a framework in which multiple transliteration models can operate simultaneously. Furthermore, there has been no comparison of the four models within the same framework and using the same data. We addressed these problems by 1) modeling the four models within the same framework, 2) comparing them under the same conditions, and 3) developing a way to improve machine transliteration through this comparison. Our comparison showed that the hybrid and correspondence-based models were the most effective and that the four models can be used in a complementary manner to improve machine transliteration performance

Character-level and syntax-level models for low-resource and multilingual natural language processing

There are more than 7000 languages in the world, but only a small portion of them benefit from Natural Language Processing resources and models. Although languages generally present different characteristics, “cross-lingual bridges” can be exploited, such as transliteration signals and word alignment links. Such information, together with the availability of multiparallel corpora and the urge to overcome language barriers, motivates us to build models that represent more of the world’s languages. This thesis investigates cross-lingual links for improving the processing of low-resource languages with language-agnostic models at the character and syntax level. Specifically, we propose to (i) use orthographic similarities and transliteration between Named Entities and rare words in different languages to improve the construction of Bilingual Word Embeddings (BWEs) and named entity resources, and (ii) exploit multiparallel corpora for projecting labels from high- to low-resource languages, thereby gaining access to weakly supervised processing methods for the latter. In the first publication, we describe our approach for improving the translation of rare words and named entities for the Bilingual Dictionary Induction (BDI) task, using orthography and transliteration information. In our second work, we tackle BDI by enriching BWEs with orthography embeddings and a number of other features, using our classification-based system to overcome script differences among languages. The third publication describes cheap cross-lingual signals that should be considered when building mapping approaches for BWEs since they are simple to extract, effective for bootstrapping the mapping of BWEs, and overcome the failure of unsupervised methods. The fourth paper shows our approach for extracting a named entity resource for 1340 languages, including very low-resource languages from all major areas of linguistic diversity. We exploit parallel corpus statistics and transliteration models and obtain improved performance over prior work. Lastly, the fifth work models annotation projection as a graph-based label propagation problem for the part of speech tagging task. Part of speech models trained on our labeled sets outperform prior work for low-resource languages like Bambara (an African language spoken in Mali), Erzya (a Uralic language spoken in Russia’s Republic of Mordovia), Manx (the Celtic language of the Isle of Man), and Yoruba (a Niger-Congo language spoken in Nigeria and surrounding countries)

Negative vaccine voices in Swedish social media

Vaccinations are one of the most significant interventions to public health, but vaccine hesitancy creates concerns for a portion of the population in many countries, including Sweden. Since discussions on vaccine hesitancy are often taken on social networking sites, data from Swedish social media are used to study and quantify the sentiment among the discussants on the vaccination-or-not topic during phases of the COVID-19 pandemic. Out of all the posts analyzed a majority showed a stronger negative sentiment, prevailing throughout the whole of the examined period, with some spikes or jumps due to the occurrence of certain vaccine-related events distinguishable in the results. Sentiment analysis can be a valuable tool to track public opinions regarding the use, efficacy, safety, and importance of vaccination

Vocabulary learning strategies : an empirical study of their use and evaluation by Saudi EFL learners majoring in English

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Reasoning with Pseudowords: How Properties of Novel Verbal Stimuli Influence Item Difficulty and Linguistic-Group Score Differences on Cognitive Ability Assessments

Pseudowords (words that are not real but resemble real words in a language) have been used increasingly as a technique to reduce contamination due to construct-irrelevant variance in assessments of verbal fluid reasoning (Gf). However, despite pseudowords being researched heavily in other psychology sub-disciplines, they have received little attention in cognitive ability testing contexts. Thus, there has been an assumption that all pseudowords work equally and work equally well for all test-takers. The current research examined three objectives with the first being whether changes to the pseudoword properties of length and wordlikeness (how much a pseudoword resembles a typical or common word in English) led to changes in item difficulty on verbal Gf items. The second objective was whether boundary conditions existed such that changes to pseudoword properties would differentially impact two linguistic sub-groups of participants – those who have English as their dominant language and those who do not have English as their dominant language. The last objective was to index and explore performance on these verbal Gf items when pseudowords were replaced with real words. Hypotheses predicting how pseudoword properties influenced item difficulty, how stimulie type – pseudoword or real word, impacted performance across linguistic sub-groups, and how linguistic sub-group status interacted with pseudoword properties were tested. Four sets of pseudowords were developed – short and wordlike, long and wordlike, short and un-wordlike, and long and un-wordlike, as well as two sets of real words – short and wordlike, and long and un-wordlike. Sixteen verbal Gf items, adapted from the LSAT, were developed to accommodate the pseudowords or real words and explore these three objectives. While none of the hypotheses were statistically significant, the results did indicate further areas of exploration. Specifically, verbal Gf items were easier when they featured longer pseudowords and more difficult when they featured un-wordlike pseudowords. Additionally, while performance of English-non-dominant participants was fairly balanced across real and pseudoword sets, English-dominant participants performed better on items featuring real words. Similarly, linguistic status interacted with wordlikeness such that English-dominant participants featured a decrease in performance as pseudowords moved from wordlike to un-wordlike. A full discussion of the findings, their implications, limitations of the current study, and directions for future research are included

Chinese elements : a bridge of the integration between Chinese -English translation and linguaculture transnational mobility

[Abstract] As the popularity of Chinese elements in the innovation of the translation part in Chinese CET, we realized that Chinese elements have become a bridge between linguaculture transnational mobility and Chinese-English translation.So, Chinese students translation skills should be critically improved; for example, on their understanding about Chinese culture, especially the meaning of Chinese culture. Five important secrets of skillful translation are introduced to improve students’ translation skills